Device metrics is of interest because the metrics provide useful information on how a device is performing as it ages. A device metric is any statistic that characterizes the device during its field life-cycle. Examples of device metrics include device failure rate, defect rate, warranty claims, return rates and life expectancy.
In the conventional art, one way in which device metrics are calculated is by using warranty data. However, a problem with using warranty data is that since the data is amorphous and does not distinguish devices on the basis of their life-cycle characteristics, the metric is not accurate. For example, the warranty approach to device metrics typically does not factor-in the actual date the device is turned-on nor does it calculate the actual hours of operation.
Thus, for accurate metrics it is necessary to qualify a device, as described herein, before its data is accepted in the calculations. Qualifying a device involves verifying that the device satisfies specified life-cycle criteria such that inclusion of its data in the calculation will not introduce inaccuracy in the metric. In the above example, if the device is in a class of devices having the same shipment month then metrics computed for this group of units would require a life cycle event that indicates the device is in operation in the field as the starting point for calculating the actual hours of operation of each device in the group. It should be noted that a device can be qualified on the basis of any of its life-cycle characteristics.
In the conventional art there is no known method or system that qualifies a device in the manner as described herein prior to using the data to calculate metrics. Thus, in a conventional art wherein warranty-based data was used, and wherein the devices were not qualified, the following disadvantages were noted: no ability to characterize the device reliability as a function of the device age; no ability to clearly trace high device failure rate estimates directly back to the time period during which the device was shipped and installed; no ability to make clear decisions pertaining to the device based on sound statistical quality control principles; less accuracy in the metric because of the assumption in the data that all units shipped in a month were installed on the first day of the next month; less accuracy in the metric because all repairs reported are posted to the current reporting month regardless of when the repairs occurred; the warranty data included repairs even when the device serial number cannot be validated with a manufacturing shipment record; the warranty data did not provided visibility to out-of-warranty devices; and the system did not use statistical control techniques to evaluate and report the metric.
Similar disadvantages are anticipated with other conventional methods and systems that do not qualify a device prior to using the data in the calculations.
Accordingly, in view of disadvantages of conventional methods, it is an objective of the present invention to provide for a more accurate way to calculate and report device metrics. These and other objectives of the present invention will no doubt become obvious to those of ordinary skill in the art on reading the following detailed description of preferred embodiments in conjunction with the various Figures.